TY - JOUR
T1 - Multi-objective optimal experimental designs for event-related fMRI studies
AU - Kao, Ming Hung
AU - Mandal, Abhyuday
AU - Lazar, Nicole
AU - Stufken, John
N1 - Funding Information:
The research of Nicole Lazar was in part supported by NSF Grant DMS-07-06192, and that of John Stufken by NSF Grant DMS-07-06917. The authors are thankful to the anonymous referees for their comments and suggestions, which resulted in an improvement of this work.
Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2009/2/1
Y1 - 2009/2/1
N2 - In this article, we propose an efficient approach to find optimal experimental designs for event-related functional magnetic resonance imaging (ER-fMRI). We consider multiple objectives, including estimating the hemodynamic response function (HRF), detecting activation, circumventing psychological confounds and fulfilling customized requirements. Taking into account these goals, we formulate a family of multi-objective design criteria and develop a genetic-algorithm-based technique to search for optimal designs. Our proposed technique incorporates existing knowledge about the performance of fMRI designs, and its usefulness is shown through simulations. Although our approach also works for other linear combinations of parameters, we primarily focus on the case when the interest lies either in the individual stimulus effects or in pairwise contrasts between stimulus types. Under either of these popular cases, our algorithm outperforms the previous approaches. We also find designs yielding higher estimation efficiencies than m-sequences. When the underlying model is with white noise and a constant nuisance parameter, the stimulus frequencies of the designs we obtained are in good agreement with the optimal stimulus frequencies derived by Liu and Frank, 2004, NeuroImage 21: 387-400. In addition, our approach is built upon a rigorous model formulation.
AB - In this article, we propose an efficient approach to find optimal experimental designs for event-related functional magnetic resonance imaging (ER-fMRI). We consider multiple objectives, including estimating the hemodynamic response function (HRF), detecting activation, circumventing psychological confounds and fulfilling customized requirements. Taking into account these goals, we formulate a family of multi-objective design criteria and develop a genetic-algorithm-based technique to search for optimal designs. Our proposed technique incorporates existing knowledge about the performance of fMRI designs, and its usefulness is shown through simulations. Although our approach also works for other linear combinations of parameters, we primarily focus on the case when the interest lies either in the individual stimulus effects or in pairwise contrasts between stimulus types. Under either of these popular cases, our algorithm outperforms the previous approaches. We also find designs yielding higher estimation efficiencies than m-sequences. When the underlying model is with white noise and a constant nuisance parameter, the stimulus frequencies of the designs we obtained are in good agreement with the optimal stimulus frequencies derived by Liu and Frank, 2004, NeuroImage 21: 387-400. In addition, our approach is built upon a rigorous model formulation.
KW - Compound design criterion
KW - Design efficiency
KW - Genetic algorithms
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U2 - 10.1016/j.neuroimage.2008.09.025
DO - 10.1016/j.neuroimage.2008.09.025
M3 - Article
C2 - 18948212
AN - SCOPUS:57649178847
SN - 1053-8119
VL - 44
SP - 849
EP - 856
JO - NeuroImage
JF - NeuroImage
IS - 3
ER -